Random Walk In 2D Graph
Use Components:
- Use matplotlib.pyplot
- Use Pandas
- Use Seaborn
- Draw Random Walk Chart
- Find Mean
- Find Degree Destribution
Introduction A random walk is a mathematical object, known as a stochastic or random process, that describes a path that consists of a succession of random steps on some mathematical space such as the integers. An elementary example of a random walk is the random walk on the integer number line, which starts at 0 and at each step moves +1 or -1 with equal probability. Other examples include the path traced by a molecule as it travels in a liquid or a gas, the search path of a foraging animal, the price of a fluctuating stock and the financial status of a gambler can all be approximated by random walk models, even though they may not be truly random in reality. As illustrated by those examples, random walks have applications to many scientific fields including ecology, psychology, computer science, physics, chemistry, biology as well as economics. Random walks explain the observed behaviors of many processes in these fields, and thus serve as a fundamental model for the recorded stochastic activity. As a more mathematical application, the value of pi can be approximated by the usage of random walk in agent-based modelling environment.
Enough with the boring theory. Let’s take a break while getting some knowledge for the code. So, to code out the random walk we will basically require some libraries in python some to do maths and some others to plot the curve.
